Climatological Studies with Doppler Radar

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Fundamental understanding of the redistribution of heat and moisture in the atmosphere is important to understanding both climate and weather. This project will undertake, and facilitate for others, fundamental studies of atmospheric convection and rainfall processes. Examples include statistical characterization of the multiple space-time scales involved; inference of vertical structures of convective heating; and comparison of different regions and regimes. Simultaneous sounding, surface, and satellite data sets will be assembled and examined to seek relationships and test hypotheses about the temporal relationships between convection and large-scale meteorological (weather regime) and climatic (land vs. sea, diurnal, seasonal) variations.Initial analyses, focused on the Tropics, will elucidate the characteristics of not only the occasional organized deep convective events, but also the occasional suppressed conditions, and the more ubiquitous conditions with shallow cumuli and middle-topped showers, which are hypothesized to have a substantial role in the general circulation.This project will compose, study, and make available to the community a carefully designed uniform hourly statistical digest of data from a large number of recent and future radar deployments. Data access has been offered by several US groups with large data archives, as well as a Japanese group with a new high-quality shipborne Doppler radar. Cylindrical binning (CYLBIN) is a novel, simple analysis technique for quantitative studies of both reflectivity data (potentially including multi-parameter data) and Doppler-derived wind and wind divergence profiles. Pilot studies with several months of shipborne radar data from the Indian, and eastern and western Pacific Oceans are promising, as significant signals are frequently present even in sparse echo fields. "Interleaved tilt angle" scanning strategies, designed by the PI with this analysis in mind, offer especially high vertical resolution in several data sets.